The Role Of Contested And Uncontested Passes In Evaluating Defensive Basketball Efficiency

The global economic impact of basketball is measured in tens of billions of dollars and requires the efficient use of resources to maximize success on and off the court. Today, coaches, players, investors, and owners need to take full advantage of modern analytical methods and digital video software capabilities to make the most efficient use of a teams resources. This research is the first in a series that makes full use of modern analytic methods and begins to define new defensive and offensive criteria to supplement the decades old game box score performance information. Data envelopment analysis and statistical methods are used to evaluate two new defensive performance metrics on defensive efficiency. The two new defensive metrics are contested and uncontested passes that are fully defined in the articles appendix. Future research will expand the sample size and allow for more comprehensive models of basketball team defensive efficiency.

[1]  R. Jewell,et al.  The Marginal Revenue Product of a Women's College Basketball Player , 2006 .

[2]  C. Barros,et al.  Performance evaluation of the English Premier Football League with data envelopment analysis , 2006 .

[3]  Jim Albert,et al.  Using Model/Data Simulations to Detect Streakiness , 2001 .

[4]  Robert A. Connolly,et al.  Skill, Luck, and Streaky Play on the PGA Tour , 2008 .

[5]  Tim Baker,et al.  THE BENEFITS OF OPTIMIZING PRICES TO MANAGE DEMAND IN HOTEL REVENUE MANAGEMENT SYSTEMS , 2003 .

[6]  Liang Liang,et al.  Measuring the Performance of Nations at Beijing Summer Olympics Using Integer-Valued DEA Model , 2010 .

[7]  Bradley P. Carlin,et al.  A Spatial Analysis of Basketball Shot Chart Data , 2006 .

[8]  Joel Sokol,et al.  A logistic regression/Markov chain model for NCAA basketball , 2006 .

[9]  Tim Baker,et al.  The Economic Payout Model for Service Guarantees , 2005, Decis. Sci..

[10]  A. Charnes,et al.  Data Envelopment Analysis Theory, Methodology and Applications , 1995 .

[11]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[12]  Ali Emrouznejad,et al.  COOPER-framework: A unified process for non-parametric projects , 2010, Eur. J. Oper. Res..

[13]  Maurizio Bevilacqua,et al.  Identifying manufacturing flexibility best practices in small and medium enterprises , 2002 .

[14]  Minwir Al-Shammari,et al.  A multi‐criteria data envelopment analysis model for measuring the productive efficiency of hospitals , 1999 .

[15]  Darryl D. Wilson,et al.  Supply management orientation and supplier/buyer performance , 2000 .

[16]  W. Cooper,et al.  Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software , 1999 .

[17]  Darryl D. Wilson,et al.  An Empirical Investigation of the Malcolm Baldrige National Quality Award Causal Model , 2000, Decis. Sci..

[18]  Julia E. Blose,et al.  Linking dimensions of service quality to organizational outcomes , 2004 .

[19]  S. Zenios,et al.  Operations, Quality, and Profitability in the Provision of Banking Services , 1999 .

[20]  John C.S. Tang,et al.  The evaluation of bank branch performance using data envelopment analysis: A framework , 2002 .

[21]  Emmanuel Thanassoulis,et al.  Introduction to the theory and application of data envelopment analysis , 2001 .

[22]  Kaoru Tone,et al.  Data Envelopment Analysis , 1996 .

[23]  Lawrence M. Seiford,et al.  Data envelopment analysis: The evolution of the state of the art (1978–1995) , 1996 .

[24]  C. Jordan Running the Numbers , 2012 .